Ensuring secure and privacy-preserving authentication in web applications remains a critical challenge due to the limitations of conventional single-factor approaches, which are vulnerable to attacks and fail to account for dynamic user behaviors. Existing multi-factor authentication (MFA) methods often rely on static rules, exposing users to unnecessary friction or weak security under evolving threat conditions. To address these gaps, this study proposes PPAB-RL, a Privacy-Preserving Adaptive Biometric framework leveraging Reinforcement Learning for intelligent MFA selection. The proposed method integrates homomorphic encryption for secure fingerprint feature storage, contextual risk scoring based on device, behavioral, and geolocation deviations, and RL-driven adaptive MFA to dynamically select authentication pathways from password-only to multi-step biometric verification. Implementation is carried out using Python, with biometric processing performed on the SOCOFing dataset containing 6,000 fingerprint images, and blockchain-enabled logging for immutable and tamper-proof audit trails. Experimental results demonstrate that PPAB-RL achieves 96.8% authentication accuracy, surpassing traditional password-only (84.2%) and fingerprint-only (93.5%) methods, while maintaining low encrypted matching overhead and minimal user friction. Ablation studies confirm the essential contribution of each module, biometric preprocessing, encryption, risk analysis, and RL-based adaptation to overall system robustness. The RL policy converges rapidly, allowing real-time adaptation to changing user behaviors and threat contexts. Overall, the proposed PPAB-RL framework establishes a highly secure, intelligent, and scalable authentication paradigm, combining encrypted biometrics, dynamic risk assessment, and blockchain validation, offering an innovative approach that can inspire further research in next-generation privacy-sensitive authentication systems.

Many sectors are quickly moving toward XR for education and professional training. These technologies, which include Virtual, Augmented, and Mixed Reality, are not being applied equally across different types of knowledge preservation. This study aims to review how effective these technologies are and whether they have overlooked the preservation of Indigenous Medicinal Knowledge. Following the PRISMA 2020 guidelines, relevant literature from 2019 to 2026 was gathered from major academic databases, including IEEE Xplore, Scopus, and ScienceDirect. After removing duplicates and non-English papers, 23 out of 39 publications met the selection criteria. The findings show that while XR is a proven technology in many sectors, it remains underused for documenting the medicinal knowledge of communities such as the Kayan and Kenyah. This lack of progress is not caused by technical limitations, but by internal organizational barriers and weak project planning. The evidence, therefore, points to a procedural problem rather than a technological one.

Alzheimer’s disease is a progressive neurodegenerative disorder for which early detection remains a significant challenge due to the complexity of clinical features and the high dimensionality of medical data. This study aims to improve the accuracy and reliability of Alzheimer’s disease detection by evaluating the performance of multiple machine learning algorithms integrated with intelligent feature selection strategies. Five classification models, Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, and Deep Learning, were investigated under two experimental scenarios: without feature selection and with feature selection using Recursive Feature Elimination, Binary Particle Swarm Optimization, and Variance Threshold. Model performance was evaluated using K-fold cross-validation based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that feature selection consistently enhances classification performance, particularly for conventional machine learning models such as Random Forest and Logistic Regression. Although the Deep Learning model achieves competitive accuracy, its reduced precision and F1-score indicate limitations when applied to reduced feature spaces. These findings highlight the importance of incorporating appropriate feature selection techniques to address data complexity and improve the effectiveness of early Alzheimer’s disease detection.

Brain image registration is fundamental for medical imaging to allow the matching of images from multiple modalities, temporal sequences, and people to offer spatial correlation. This is crucial for activities such as cohort studies, intervention recommendations, and treatment monitoring, where exact alignment assures consistent analysis. Notwithstanding their importance, modern brain image registration techniques have many shortcomings, including limited resistance to noise, misalignment in multi-modality images, and costly computational expenses. These limits may impede real-time clinical environment practical implementation and provide less than optimal registration accuracy. This study addresses these issues by means of an Improved Brain Image Registration Technique Using Machine Learning Algorithms (BIRT-MLA). The proposed architecture detects significant image properties by means of convolutional neural networks (CNNs), therefore enabling feature extraction. By applying a supervised learning method, it guarantees precise alignment even in noisy and demanding imaging situations by forecasting transformation parameters. Lowering the registration error by modern optimization techniques helps to save processing time and maintain remarkable accuracy even in this respect. Using CNNs, the proposed method helps to effectively classify brain images, thereby improving diagnostic support and the usefulness of registered images for downstream operations. Improving clinical judgment and simplifying processes rely on grouping registration and categorization into a logical sequence. By means of enhanced alignment precision, resistance to picture faults, and shortened computing time compared to current approaches, experimental findings expose the advantages of the suggested technology. This development may be very useful in clinical and experimental settings, thereby supporting the accuracy and efficiency of brain picture analysis.

This systematic literature review examines recent developments in stock market prediction using heterogeneous data sources that combine technical indicators, fundamental attributes, and sentiment-driven signals. Despite the growing adoption of machine learning in financial forecasting, existing research remains fragmented across data modalities, fusion strategies, and evaluation protocols, limiting comparability and practical applicability. Studies published between 2018 and 2024 were retrieved from five major scholarly databases and screened based on predefined eligibility criteria, resulting in 44 peer-reviewed articles included in the final analysis. The review synthesizes the quantitative and qualitative data modalities employed, the machine learning and deep learning methodologies adopted, the evaluation metrics used to assess predictive performance, and the principal challenges associated with multi-source stock market prediction. Findings reveal a clear shift toward deep learning architectures, hybrid fusion techniques, and the integration of external information such as news, corporate disclosures, and social media sentiment. Despite this progress, the literature exhibits inconsistent evaluation practices, limited attention to temporal data leakage, and insufficient coverage of non-English and emerging markets. This review consolidates current knowledge, presents a structured taxonomy of heterogeneous data sources and fusion strategies, and identifies open research challenges to guide future work in multimodal stock market prediction.

Precision agriculture enables data-driven crop monitoring and improved resource utilization. Paddy cultivation requires continuous surveillance and timely fertilizer application because it is sensitive to soil nutrient dynamics, water availability, and climatic conditions. Conventional practices such as manual field inspection and heuristic fertilizer advisory methods are often labor-intensive and subjective, which can reduce decision consistency and contribute to yield variability. To address these limitations, this study proposes a Selfdom Enhanced CatBoost (SECB) framework for remote paddy growth-stage monitoring and fertilizer recommendation. Multispectral remote sensing data collected over multiple seasons are used to compute vegetation indices, including NDVI, GNDVI, RVI, GRVI, and NDRE, to characterize crop vigor and chlorophyll-related variation across growth stages. The proposed SECB improves CatBoost by integrating an Improved Osprey Optimization Algorithm (IOOA) to tune key model parameters, aiming to enhance feature interaction learning and reduce overfitting. In addition, oppositional function-based initialization is applied to improve the exploration capability of IOOA and accelerate convergence. Experimental results show that SECB achieves improved performance over baseline classifiers in terms of accuracy, precision, F1-score, specificity, and AUC. The proposed approach provides reliable growth-stage identification and supports fertilizer recommendations to promote efficient nutrient usage and improved productivity. Overall, the framework offers an automated and scalable decision-support strategy for paddy crop management.

Breast cancer remains a highly heterogeneous disease for which it demands advanced computational techniques that can reveal significant biological patterns in high-dimensional epigenomic data. DNA methylation profiles generated by the Illumina HumanMethylation450 platform yield rich, clinically relevant signals but introduce significant analytical challenges due to their high dimensionality, sparsity, and nonlinear structure. This work presents a novel memory-efficient hybrid learning architecture that combines Truncated Singular Value Decomposition (SVD), a deep Autoencoder, and a multi-model ensemble classifier for boosting subtype classification performance using TCGA-BRCA methylation data. In order to circumvent memory limits and prevent system crashes, a probe-subset extraction strategy combined with variance-based feature selection was employed to ensure fast and safe data loading from the Xena repository. While the autoencoder extracts compact nonlinear manifold representations, SVD captures the global linear variance structure. Further, the fused latent space is modelled by an ensemble including Random Forest, XGBoost, and a lightweight Keras neural classifier that allows the system to exploit different decision limits and achieve robust generalization. The experimental investigation across several architectures demonstrates high predictive performance with ROC-AUC scores exceeding 0.99 and accuracies higher than 0.96 for Basic CNN and MLP models. Furthermore, the proposed hybrid ensemble improves stability and precision by outperforming traditional baselines and confirming the complementary nature of spectral and deep feature extraction. This study is suitable for large-scale biomedical data analytics scenarios. In conclusion, this work provides an efficient hybrid machine learning framework for breast cancer methylation study by offering a strong platform for improved prognostic modelling and development of epigenetic biomarkers.

South Sumatra songket motifs present a challenging fine-grained classification task due to high inter-class similarity and substantial intra-class variability. This study proposes the Ghost-Vanilla Feature Map, a novel hybrid architecture that integrates low-cost ghost-generated features with the lightweight structural stability of VanillaNet to enhance discriminative feature learning while reducing computational burden. The proposed architecture is designed to address the inefficiency of conventional convolution-heavy networks in capturing subtle motif variations. Experimental evaluation on a dataset comprising 20 songket motif classes demonstrates that a ghost ratio 2 achieves the best trade-off, attaining an accuracy of 0.98 with more than 75% parameter reduction. Increasing the ghost ratio to 3 preserves high classification performance with an accuracy of 0.97, while ratios 4 and 5 further reduce model size at the expense of marginal accuracy degradation. Comparative results indicate that the Ghost-Vanilla Feature Map consistently outperforms lightweight CNN baselines, including MobileNetV3-Small, MobileNetV4-Conv-Small, EfficientNetV2-Small, and ShuffleNetV2. The proposed architecture substantially surpasses the Vanilla-only baseline, which achieves an accuracy of only 0.860 despite requiring 30.19 million parameters, highlighting the limitations of conventional convolution-dominant designs in fine-grained textile classification. The hybrid configuration with a ghost ratio 2 delivers superior accuracy while nearly halving the parameter count and significantly reducing computational overhead. Overall, the Ghost-Vanilla Feature Map provides an efficient and highly discriminative solution for fine-grained songket motif classification, achieving strong performance while substantially reducing model complexity through a balanced hybrid representation.

The accelerated progression of information technology has necessitated the adoption of more intelligent and adaptive Enterprise Systems (ES) to sustain and optimize organizational processes. The ES serve as critical infrastructures for resource management, operational efficiency, and sustaining competitive advantage; however, their deployment frequently encounters persistent challenges. These include discrepancies between modelled and actual business processes, insufficient visibility in process execution, and limited automation in the detection and optimization of workflows. To mitigate these limitations, this study advances an Artificial Intelligence (AI)-enabled Process Mining paradigm. This approach facilitates the systematic extraction, analysis, and visualization of business processes, thereby supporting the identification of deviations, the detection of anomalies, and the provision of data-driven recommendations for continuous improvement. The overarching aim of the research is to conceptualize and evaluate an enterprise system framework that integrates AI-driven Process Mining to reinforce transparency, efficiency, and effectiveness in business process management. The proposed framework aims to provide automated analytical capabilities, predictive insights, and a robust data-centric foundation to enhance the precision of strategic decision-making, thereby contributing to the advancement of adaptive and intelligent enterprise systems.

Ensuring strong security readiness in telemedicine is essential to protect patients, safeguard their data, and build confidence and satisfaction in today’s digital healthcare platforms. Hence, this research explores and evaluates the maturity of telemedicine services from the perspectives of ICT professionals focused on security readiness. A case study with a stratified survey was conducted among ICT personnel in public hospitals in Malaysia, revealing critical gaps by examining professionals in authority for system security, data fortification, infrastructure resilience, and operational endurance of the telemedicine platform. The UTAUT model employed in this study incorporates two additional constructs, Trust in Technology and Adaptability. Trust in technology captures confidence in system safety, data privacy, and dependability. The adaptability, focused on system technical areas and user-related factors, towards secure telemedicine adoption. The SPSS Statistics Version 21 was used to analyze the data, retaining descriptive statistics, reliability testing, correlation analysis, and multiple regression. Results indicated a generally high level of security readiness among ICT professionals, with strong Trust in Technology and Adaptability to telemedicine systems. Facilitating Conditions, particularly the availability of secure infrastructure and technical safeguards, emerged as the most influential factor and significantly predicted Performance Expectancy. Although Trust in Technology and Adaptability exhibited strong positive correlations with key UTAUT constructs, their effects were statistically significant in the regression analysis, supporting all proposed hypotheses, including their influence on Satisfaction, which suggests a direct role in shaping security readiness.